kmeans集群后查找功能重要性的python代码

我研究了找到特性重要性的方法（我的数据集只有9个特性）

我希望对影响集群形成的每个特征进行排名

计算每个尺寸的质心方差。方差最大的维度在区分聚类时最为重要

如果您只有少量的变量，您可以执行某种类型的遗漏测试（删除1个变量并重做集群）。还要记住，k-means依赖于初始化，所以在重做集群时，您希望保持该值不变

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任何python代码都可以实现这一点吗？

请考虑这样做功能选择

import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2

# UNIVARIATE SELECTION

data = pd.read_csv('C:\\Users\\Excel\\Desktop\\Briefcase\\PDFs\\1-ALL PYTHON & R CODE SAMPLES\\Feature Selection - Machine Learning\\train.csv')
X = data.iloc[:,0:20]  #independent columns
y = data.iloc[:,-1]    #target column i.e price range

#apply SelectKBest class to extract top 10 best features
bestfeatures = SelectKBest(score_func=chi2, k=10)
fit = bestfeatures.fit(X,y)
dfscores = pd.DataFrame(fit.scores_)
dfcolumns = pd.DataFrame(X.columns)
#concat two dataframes for better visualization 
featureScores = pd.concat([dfcolumns,dfscores],axis=1)
featureScores.columns = ['Specs','Score']  #naming the dataframe columns
print(featureScores.nlargest(10,'Score'))  #print 10 best features


# FEATURE IMPORTANCE
data = pd.read_csv('C:\\your_path\\train.csv')
X = data.iloc[:,0:20]  #independent columns
y = data.iloc[:,-1]    #target column i.e price range
from sklearn.ensemble import ExtraTreesClassifier
import matplotlib.pyplot as plt
model = ExtraTreesClassifier()
model.fit(X,y)
print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers
#plot graph of feature importances for better visualization
feat_importances = pd.Series(model.feature_importances_, index=X.columns)
feat_importances.nlargest(10).plot(kind='barh')
plt.show()

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到目前为止，您尝试了什么，以及您的尝试出了什么问题？请提供一个答案，但问题是关于k的意思，上面是一个监督问题吗？这两个联系如何？这个答案只有在你知道你的依赖变量的情况下才有效，这表明了一个有监督的问题，而不是k-均值聚类的情况，它是无监督的

# Correlation Matrix with Heatmap
data = pd.read_csv('C:\\your_path\\train.csv')
X = data.iloc[:,0:20]  #independent columns
y = data.iloc[:,-1]    #target column i.e price range
#get correlations of each features in dataset
corrmat = data.corr()
top_corr_features = corrmat.index
plt.figure(figsize=(20,20))
#plot heat map
g=sns.heatmap(data[top_corr_features].corr(),annot=True,cmap="RdYlGn")

Dataset is available here:

https://www.kaggle.com/iabhishekofficial/mobile-price-classification#train.csv